Information
Integration Theory
This theory was developed, and extensively tested through a
variety of experiments, by Norman Anderson (1971,
1981a,
1981b,
1991; see also
Fishbein,
1967). Information Integration
theory explores how attitudes are formed and changed through
the integration (mixing, combining) of new information with
existing cognitions or thoughts.

Information integration theory considers the ideas in a
persuasive message to be pieces of information, and each
relevant piece of information has two qualities: value and
weight. The value of a bit of information is its evaluation
(favorable or unfavorable) and the weight is the
information’s perceived importance. For example, Steve tells
Sarah that Joe has a ponytail. The value of this information
is whether Sarah thinks a ponytail (for Joe) is good
(attractive) or bad (unattractive or inappropriate). The
weight is how much that friend’s hair style matters to
Sarah. If it does matter (has some weight) and if Sarah thinks
it is good for Joe to wear a ponytail, then this piece of
information inclines Sarah to have a favorable attitude toward
this friend.

However, Sarah’s new attitude would also depend on what she
thought about Joe before she learned about Joe’s new hair
style. If she previously had a favorable attitude toward Joe,
her attitude would remain favorable. It could be come even
more favorable, especially if she thought hair style was very
important (if this information had a larger weight) and if
Sarah really, really liked pony tails (if the information had
a high positive value). On the other hand, if Sarah used to
have an unfavorable attitude toward Joe, this new information
probably wouldn’t change her attitude from unfavorable to
favorable. It could mean that her new attitude wasn’t as
negative as before, especially if this new information had a
large weight and a high positive value.

On the other hand, it is possible that Sarah doesn’t think
men should wear ponytails. This would mean that the new
information had a negative value. Again, Sarah’s new
attitude would depend on three factors: her original attitude,
the value of the new information to Sarah, and its weight. If
she liked Joe before she learned about his ponytail, she might
like him less (have a less favorable attitude). Her attitude
is most likely to change if men’s hair style is important to
her (has weight) and if she has a very unfavorable feeling
about ponytails on men (value). If her initial attitude was
unfavorable, finding out about Joe’s new hair style would
have a tendency to make her new attitude even more
unfavorable. If the weight of this new information was high
and the value was very unfavorable, Sarah’s attitude could
become noticeably more negative.

This, Information Integration Theory states that when we
obtain new information (often from persuasive messages), those
new pieces of information will affect our attitudes. They
won’t replace our existing attitudes: If Sarah began with an
unfavorable attitude toward Joe and she likes ponytails on
men, she won’t all of a sudden have a strong positive
attitude toward Joe. However, when we learn new positive
information, negative attitudes tend to become less negative
and attitudes that are positive are likely to become somewhat
more positive.

Furthermore, Information Integration Theory tells us that each
bit of information has two important qualities, weight and
value. Both factors influence our attitudes. Information that
is (1) high in value, highly favorable (or highly
unfavorable), and (2) high in weight (is very important to us)
will have more influence on our attitudes than information low
in value or weight. Information with low value (slightly
favorable or slightly unfavorable) and low weight will have
the least influence on our attitudes.

Therefore, new information is mixed, combined, or integrated
with existing information to create a new attitude. However,
information can be combined in more than one way. One
important question is whether new information is added
to existing knowledge, or whether it is averaged into
it. Consider this simple example. Bob has a pretty favorable
attitude of +3 (on a scale of -5 to +5) toward a certain
automobile. If he learns a new piece of information (say, it
has chrome wheels) that is slightly favorable for him, say a
+1, what will his new attitude be?If he adds +1 and +3, then Bob’s new attitude
will be more favorable than his existing attitude, a +4. On
the other hand, if Bob averages the new and old
information his new attitude should be less favorable, a +2 (1
plus 3 is 4, divided by 2 pieces of information, equals an
average of 2).

Some people believe that the adding model is best. But what
happens if one has several pieces of new information, all
valued at +3 (again, on a scale of -5 to +5). If Bob is told
four new pieces of information that he values at +3 each, his
attitude would be +3 (his initial attitude) +3 +3 +3 +3, or
+15. But if the attitude scale goes from -5 to +5, he can’t
possibly have an attitude of over +5. And research shows that
in situations like this one Bob’s final attitude wouldn’t
even be +5.

If adding doesn’t work, does this mean that information is
combined by averaging?If
he starts with a +3 and learns four new pieces of information,
all valued at +3, averaging this information (+3, the initial
attitude, added to +3 +3 +3 +3 and then divided by 5) would
produce a final attitude of +3. But surely if Bob learns
several new favorable pieces of information about this car his
attitude would become somewhat more positive. And, again, the
research shows that in these kinds of situations Bob’s final
attitude would be higher than +3.

Many tests have been tried to decide this question but the
evidence does not clearly support either adding or averaging
models. In my opinion, this is true is because human beings
aren’t computers or calculators. I certainly agree that
people do combine new information and old to create new
attitudes. However, I do not believe that people assign
numbers to pieces of information or perform mathematical
calculations (adding or averaging) to figure out their new
attitudes. I think that formulas should be considered to be approximations
of what human beings do without numbers. To make a formula
work, we have to put numbers into it and combine those numbers
in some way (adding or averaging them). These theories and
formulas do come close to predicting our attitudes, so they
are useful. But we shouldn’t be surprised if these formulas
do not predict exact attitudes. I think it is enough
that they can come close.

I kept these examples about Sarah and Bob simple. But many
attitudes are complex and often we have both positive and
negative ideas about people or cars. A attitude toward a car
that is favorable overall may be made up from both favorable
(affordable, sporty, nice color, anti-lock brakes, fast,
handles well) and unfavorable (too little cargo room, no CD
player, poor gas mileage). For the overall attitude to be
favorable the positive ideas must be more numerous or have
higher weight and value than the unfavorable ideas (or be all
three: positive ideas are more numerous, have higher weight,
and higher value than the negative ideas).